CS533 Modeling and Performance Evaluation of Network and Computer Systems

Transcription

1 Let s Get Started! CS533 Modeling and Performance Evaluation of Network and Computer Systems Introduction (Chapters 1 and 2) Describe a performance study you have done Work or School or Describe a performance study you have recently read about Research paper Newspaper article Scientific journal And list one good thing or one bad thing about it Objectives (1 of 6) Select appropriate evaluation techniques, performance metrics and workloads for a system. Techniques: measurement, simulation, analytic modeling Metrics: criteria to study performance (ex: response time) s: requests by users/applications to the system Example: What performance metrics should you use for the following systems? a) Two disk drives b) Two transactions processing systems c) Two packet retransmission algorithms Objectives (2 of 6) Conduct performance measurements correctly Need two tools: load generator and monitor Example: Which workload would be appropriate to measure performance for the following systems? a) Utilization on a LAN b) Response time from a Web server c) Audio quality in a VoIP network Objectives (3 of 6) Use proper statistical techniques to compare several alternatives One run of workload often not sufficient Many non-deterministic computer events that effect performance Comparing average of several runs may also not lead to correct results Especially if variance is high Example: Packets lost on a link. Which link is better? File Size Link A Link B

2 Objectives (4 of 6) Design measurement and simulation experiments to provide the most information with the least effort. Often many factors that affect performance. Separate out the effects that individually matter. Example: The performance of a system depends upon three factors: A) garbage collection technique: G1, G2 none B) type of workload: editing, compiling, AI C) type of CPU: P2, P4, Sparc How many experiments are needed? How can the performance of each factor be estimated? Objectives (5 of 6) Perform simulations correctly Select correct language, seeds for random numbers, length of simulation run, and analysis Before all of that, may need to validate simulator Example: To compare the performance of two cache replacement algorithms: A) how long should the simulation be run? B) what can be done to get the same accuracy with a shorter run? Objectives (6 of 6) Select appropriate evaluation techniques, performance metrics and workloads for a system. Conduct performance measurements correctly. Use proper statistical techniques to compare several alternatives. Design measurement and simulation experiments to provide the most information with the least effort. Use simple queuing models to analyze the performance of systems. The Art of Performance Evaluation Evaluation cannot be produced mechanically Requires intimate knowledge of system Careful selection of methodology, workload, tools No one correct answer as two performance analysts may choose different metrics or workloads Like art, there are techniques to learn how to use them when to apply them Example: Comparing Two Systems Two systems, two workloads, measure transactions per second Work- Work- System load 1 load 2 A B Which is better? 2

3 Example: Comparing Two Systems Two systems, two workloads, measure transactions per second They are equally good! but is A better than B? The Ratio Game Take system B as the base Work- Work- System load 1 load 2 Average A B System 1 2 Average A B A is better! but is B better than A? Common Mistakes (1 of 3) Undefined Goals There is no such thing as a general model Describe goals and then design experiments (Don t shoot and then draw target) Biased Goals Don t show YOUR system better than HERS (Performance analysis is like a jury) Unrepresentative Should be representative of how system will work in the wild Ex: large and small packets? Don t test with only large or only small Common Mistakes (2 of 3) Wrong Evaluation Technique Use most appropriate: model, simulation, measurement (Don t have a hammer and see everything as a nail) Inappropriate Level of Detail Can have too much! Ex: modeling disk Can have too little! Ex: analytic model for congested router No Sensitivity Analysis Analysis is evidence and not fact Need to determine how sensitive results are to settings Common Mistakes (3 of 3) Improper Presentation of Results It is not the number of graphs, but the number of graphs that help make decisions Omitting Assumptions and Limitations Ex: may assume most traffic TCP, whereas some links may have significant UDP traffic May lead to applying results where assumptions do not hold 3

4 A Systematic Approach 1. State goals and define boundaries 2. Select performance metrics 3. List system and workload parameters 4. Select factors and values 5. Select evaluation techniques 6. Select workload 7. Design experiments 8. Analyze and interpret the data 9. Present the results. Repeat. State Goals and Define Boundaries Just measuring performance or seeing how it works is too broad Ex: goal is to decide which ISP provides better throughput Definition of system may depend upon goals Ex: if measuring CPU instruction speed, system may include CPU + cache Ex: if measuring response time, system may include CPU + memory + + OS + user workload Select Metrics Criteria to compare performance In general, related to speed, accuracy and/or availability of system services Ex: network performance Speed: throughput and delay Accuracy: error rate Availability: data packets sent do arrive Ex: processor performance Speed: time to execute instructions List Parameters List all parameters that affect performance System parameters (hardware and software) Ex: CPU type, OS type, parameters Ex: Number of users, type of requests List may not be initially complete, so have working list and let grow as progress Select Factors to Study Divide parameters into those that are to be studied and those that are not Ex: may vary CPU type but fix OS type Ex: may fix packet size but vary number of connections Select appropriate levels for each factor Want typical and ones with potentially high impact For workload often smaller (1/2 or 1/10 th ) and larger (2x or 10x) range Start small or number can quickly overcome available resources! 4

5 Select Evaluation Technique Depends upon time, resources and desired level of accuracy Analytic modeling Quick, less accurate Simulation Medium effort, medium accuracy Measurement Typical most effort, most accurate Note, above are all typical but can be reversed in some cases! Select Set of service requests to system Depends upon measurement technique Analytic model may have probability of various requests Simulation may have trace of requests from real system Measurement may have scripts impose transactions Should be representative of real life Design Experiments Want to maximize results with minimal effort Phase 1: Many factors, few levels See which factors matter Phase 2: Few factors, more levels See where the range of impact for the factors is Analyze and Interpret Data Compare alternatives Take into account variability of results Statistical techniques Interpret results. The analysis does not provide a conclusion Different analysts may come to different conclusions Present Results Make it easily understood Graphs Disseminate (entire methodology!) "The job of a scientist is not merely to see: it is to see, understand, and communicate. Leave out any of these phases, and you're not doing science. If you don't see, but you do understand and communicate, you're a prophet, not a scientist. If you don't understand, but you do see and communicate, you're a reporter, not a scientist. If you don't communicate, but you do see and understand, you're a mystic, not a scientist." 5

6 Case Study Consider remote pipes (rpipe) versus remote procedure calls (rpc) rpc is like procedure call but procedure is handled on remote server Client caller blocks until return rpipe is like pipe but server gets output on remote machine Client process can continue, non-blocking Goal: study the performance of applications using rpipes to similar applications using rpcs System Definition Client and Server and Network Key component is channel, either a rpipe or an rpc Only the subset of the client and server that handle channel are part of the system Client Network Server - Try to minimize effect of components outside system Services There are a variety of services that can happen over a rpipe or rpc Choose data transfer as a common one, with data being a typical result of most client-server interactions Classify amount of data as either large or small Thus, two services: Small data transfer Large data transfer Metrics Limit metrics to correct operation only (no failure or errors) Study service rate and resources consumed A) elapsed time per call B) maximum call rate per unit time C) Local CPU time per call D) Remote CPU time per call E) Number of bytes sent per call System Speed of CPUs Local Remote Network Speed Reliability (retrans) Operating system overhead For interfacing with channels For interfacing with network Parameters Time between calls Number and sizes of parameters of results Type of channel rpc Rpipe Other loads On CPUs On network Key Factors Type of channel rpipe or rpc Speed of network Choose short (LAN) across country (WAN) Size of parameters Small or larger Number of calls 11 values: 8, 16, All other parameters are fixed (Note, try to run during light network load) 6

7 Evaluation Technique Since there are prototypes, use measurement Use analytic modeling based on measured data for values outside the scope of the experiments conducted Synthetic program generated specified channel requests Will also monitor resources consumed and log results Use null channel requests to get baseline resources consumed by logging (Remember the Heisenberg principle!) Experimental Design Full factorial (all possible combinations of factors) 2 channels, 2 network speeds, 2 sizes, 11 numbers of calls 2 x 2 x 2 x 11 = 88 experiments Data Analysis Analysis of variance will be used to quantify the first three factors Are they different? Regression will be used to quantify the effects of n consecutive calls Performance is linear? Exponential? 7